I build software systems that solve real-world problems — from full-stack applications and SaaS platforms to business management systems and machine learning experiments.

My current direction is combining:

Software Engineering
        +
System Architecture
        +
Business Systems
        +
Data
        +
Artificial Intelligence

I enjoy understanding how systems work underneath the abstraction rather than simply using frameworks.

• 💻 Software Engineer focused on full-stack development
• 🚀 Building SaaS and business management platforms
• 🏢 Interested in ERP / Business Operating Systems
• 🤖 Researching Artificial Intelligence and Machine Learning
• 🧠 Learning neural networks from mathematical fundamentals
• 📊 Building data-driven systems and analytics
• ☕ Java / Spring Boot backend development
• 🌐 Vue / React / Node.js full-stack development
• 🗄️ MySQL / MongoDB database development
• ☁️ Cloud deployment and server infrastructure
• ⚙️ Interested in automation, scalable architecture, and intelligent software

• Component architecture
• Responsive UI/UX
• Admin dashboards
• Business applications
• Data visualization
• State management
• API integration
• Authentication flows
• Real-time interfaces

Java is one of my core backend technologies.

• Spring Boot
• Spring Data JPA
• Spring Security
• REST APIs
• JDBC
• MySQL integration
• CRUD architecture
• Authentication / Authorization
• Database relationships
• Java Swing
• Maven
• JUnit
• Mockito
• Logging
• Exception handling

• REST API design
• Authentication
• Authorization
• JWT
• OAuth
• CRUD systems
• Business logic
• API architecture
• Database integration
• Validation
• Error handling
• Scalable backend services

• Relational database design
• MongoDB document modeling
• MySQL
• MongoDB Atlas
• JPA / Hibernate
• Mongoose
• Database relationships
• Indexing
• Query optimization
• Data validation

• Linux servers
• DigitalOcean
• Nginx
• PM2
• Certbot
• SSH
• Git / GitHub
• Environment configuration
• Server deployment
• Reverse proxies
• SSL configuration

One of my current research directions is understanding how machine learning actually works underneath modern AI frameworks.

Instead of starting with high-level libraries, I am working from the fundamentals.

Mathematics
     ↓
Algorithms
     ↓
Perceptrons
     ↓
Neural Networks
     ↓
Optimization
     ↓
Deep Learning
     ↓
AI Systems

Building a perceptron from scratch using Python.

Current experiment:

Input
[x, y, bias]
      ↓
Weighted Sum
      ↓
Activation
      ↓
Prediction
      ↓
Error
      ↓
Weight Adjustment
      ↓
Learning

The model learns to classify points:

🟢 Above the line
🔴 Below the line

without being explicitly given the final decision boundary.

Currently studying:

• Neurons
• Inputs
• Weights
• Bias
• Activation functions
• Forward propagation
• Loss / error
• Learning rate
• Gradient descent
• Backpropagation
• Hidden layers
• Output layers
• Matrix operations
• Optimization

My goal is to gradually move from a simple perceptron toward deeper machine-learning systems.

• Inputs
• Weights
• Bias
• Weighted sum
• Activation function
• Prediction
• Error calculation
• Weight adjustment
• Learning rate
• Training loop

• Training data visualization
• Decision boundary
• Random data generation
• Large datasets
• Real-time training visualization
• Weight history
• Error history
• Accuracy graph
• Epoch visualization

• Multiple neurons
• Hidden layers
• Forward propagation
• Backpropagation
• Gradient descent
• Loss functions
• ReLU
• Sigmoid
• Softmax
• Multi-class classification

• Matrix-based neural networks
• Mini-batch training
• Optimizers
• CNN
• RNN
• Transformers
• Attention mechanisms

Researching how AI can be applied to:

• Business intelligence
• ERP systems
• Demand forecasting
• Inventory forecasting
• Sales prediction
• Anomaly detection
• Recommendation systems
• Business automation
• Decision support systems
• Intelligent analytics

One of my main interests is combining AI with business software.

Traditional ERP:

Business
   ↓
Data
   ↓
Reports
   ↓
Human Decision

The direction I am researching:

Business
   ↓
Data
   ↓
AI / ML
   ↓
Pattern Detection
   ↓
Prediction
   ↓
Recommendation
   ↓
Human Decision

For example:

Inventory Data
      ↓
Historical Sales
      ↓
Purchasing Data
      ↓
Seasonality
      ↓
Neural Network
      ↓
Demand Forecast
      ↓
Recommended Purchase

The goal is not simply to build software that stores business information.

The goal is to build systems that can learn from business data and help people make better decisions.

I am particularly interested in turning raw business data into useful information.

Raw Data
   ↓
Data Processing
   ↓
Analytics
   ↓
Patterns
   ↓
Predictions
   ↓
Business Decisions

Examples:

• Sales forecasting
• Inventory optimization
• Cost analysis
• Profit analysis
• Customer behavior
• Purchase prediction
• Waste detection
• Business performance analysis

A machine-learning learning laboratory built from scratch in Python.

• Perceptron
• Random weights
• Bias
• Activation function
• Error calculation
• Learning rate
• Weight adjustment
• Training epochs
• Accuracy testing
• Random dataset generation
• Decision boundary visualization

Classify points:

Above a line → +1

Below a line → -1

Python
Random
Matplotlib

A full-stack business management system designed around restaurant operations.

• 📦 Inventory Management
• 🛒 Purchase Orders
• 🧾 POS
• 💰 Sales Tracking
• 📊 Analytics
• 📈 Business Reports
• 🗑️ Waste Management
• 👨‍🍳 Recipe Management
• 👥 Customer Management
• 🤖 Data-driven Insights

Vue.js
Tailwind CSS
Node.js
Express.js
MongoDB
Chart.js

A business operating platform designed to bring multiple business functions into one system.

• Authentication
• Authorization
• User management
• Business management
• Branch management
• Inventory
• Purchasing
• Sales
• Accounting
• Customers
• Reports
• Analytics
• Automation
• AI-assisted insights

Vue / React
Node.js
Express
Spring Boot
MySQL
MongoDB

A Java backend project focused on learning database-driven application architecture.

Java Application
      ↓
JDBC
      ↓
MySQL
      ↓
CRUD
      ↓
Business Logic
      ↓
Validation
      ↓
Testing
      ↓
Deployment

• Java
• JDBC
• MySQL
• CRUD
• PreparedStatement
• Exception handling
• Logging
• JUnit
• Mockito
• Database design

CREATE
   ↓
READ
   ↓
UPDATE
   ↓
DELETE

The project is designed as a foundation for larger Java backend applications.

My development approach focuses on understanding the entire system rather than only one layer.

                    ┌───────────────┐
                    │   Frontend    │
                    └───────┬───────┘
                            │
                            ▼
                    ┌───────────────┐
                    │   REST API    │
                    └───────┬───────┘
                            │
                            ▼
                    ┌───────────────┐
                    │ Business Logic│
                    └───────┬───────┘
                            │
                            ▼
                    ┌───────────────┐
                    │   Database    │
                    └───────────────┘
                            │
                            ▼
                    ┌───────────────┐
                    │ AI / Analytics│
                    └───────────────┘

• System Design
• Distributed Systems
• API Architecture
• Database Architecture
• Scalability
• Performance Optimization
• Security
• Cloud Infrastructure

• Machine Learning
• Neural Networks
• Deep Learning
• Optimization
• Computer Vision
• Natural Language Processing
• Generative AI
• AI Agents
• Predictive Analytics

• Demand Forecasting
• Recommendation Systems
• Anomaly Detection
• Business Intelligence
• Predictive Analytics
• Automated Decision Support
• AI-powered ERP

I prefer to understand the fundamentals before relying on abstractions.

For example:

Don't just use:

NeuralNetwork()

Understand:

Input
 ↓
Weight
 ↓
Weighted Sum
 ↓
Activation
 ↓
Error
 ↓
Gradient
 ↓
Weight Update

The same philosophy applies to software engineering:

Don't just use a framework.

Understand:

HTTP
 ↓
API
 ↓
Architecture
 ↓
Database
 ↓
Networking
 ↓
Security
 ↓
Infrastructure

Build software that creates measurable business value.

I believe good software should be:

• ✅ Simple to use
• ✅ Easy to maintain
• ✅ Reliable
• ✅ Secure
• ✅ Scalable
• ✅ Data-driven
• ✅ Designed around real problems
• ✅ Built for long-term evolution

"AI is the new electricity." — Andrew Ng

My goal is not simply to "use AI."

I want to understand how AI works, how it can be engineered into real systems, and where it can create meaningful value.

My long-term technical direction sits at the intersection of:

              SOFTWARE
                  │
                  │
        ┌─────────┴─────────┐
        │                   │
     SYSTEMS              DATA
        │                   │
        └─────────┬─────────┘
                  │
                  ▼
              ARTIFICIAL
             INTELLIGENCE
                  │
                  ▼
          BUSINESS SYSTEMS
                  │
                  ▼
          INTELLIGENT SOFTWARE

The ultimate goal is to build software that doesn't just record what happened.

It should increasingly help answer:

What happened?
        ↓
Why did it happen?
        ↓
What will happen?
        ↓
What should we do?

I enjoy taking an idea from:

"I wonder if this is possible..."

to:

Research
   ↓
Prototype
   ↓
Code
   ↓
Experiment
   ↓
System
   ↓
Product

Learn.
Build.
Break.
Research.
Improve.
Repeat.

The goal isn't to know every technology. The goal is to understand how to build.